SUMMARY/ABSTRACT: PROJECT 2 Alzheimer's disease (AD) is well known to have a strong genetic basis. Recent genome wide association studies (GWAS) of AD have identified at least 20 regions of the human genome that are linked with developing AD. Currently it is not known why these regions are associated with AD. To fill in this knowledge gap, we propose to sequence genes in and around the 20 GWAS signals to identify all potential AD-risk variants. We propose to do this in a discovery dataset (n=1,052) and replication dataset (n=504), separately and combine our analysis together in a meta-analysis. These unique datasets are entirely comprised of individuals who have undergone phenotypic and pathologic characterization from three large community- and clinic-based studies and resources from four Alzhiemer's Disease Research Centers (ADRCs), including the Emory ADRC. Our overarching hypothesis is that some AD GWAS signals are due to one or more coding variants that makes the translated protein more likely to aggregate. We are uniquely poised to address this hypothesis given the unique datasets of brain tissue and our expertise in both large-scale and targeted proteomic analyses. By combining our genetic sequencing data with cutting-edge mass spectrometry we will sequence encoded protein products of the same genes from individuals that underwent genetic sequencing. This will allow us to ask which genetic variants associate with AD and whether those genetic variants influence the abundance or aggregation potential of proteins in the brains of individuals with AD. Furthermore, we have developed a new method to detect and measure novel proteins that result from genetic sequence variants that cause a change the primary amino acid sequence. This gives us the ability to directly test whether variant containing proteins are more or less abundant or aggregation prone in carriers versus non-carriers and determine whether that contributes to AD risk. Overall, this work is highly likely to help unravel why some of the 20 GWAS target regions are associated with AD and provide testable models for how genetic variants influence the encoded protein's lifecycle and contribute to disease in individuals with AD.